TWI677843B - Intelligent cluster suggestion system and method - Google Patents

Abstract

An intelligent cluster analysis system includes a server unit and a terminal electronic device. The server unit generates multiple transaction characteristic data according to multiple customer transaction records, and each transaction characteristic data includes multiple transaction characteristic values (one of which is a total profit or loss). The server unit generates a plurality of cluster definition data based on the transaction characteristic data and divides the transaction characteristic data into a plurality of clusters. For each cluster, the server unit selects, among the transaction characteristic data of the cluster, the transaction characteristic information of which the percentile of the total profit or loss in the cluster meets a predetermined percentile range, respectively. Target transaction characteristics. For each cluster, the server unit generates a reference data according to the transaction characteristic values of the target transaction characteristic data of the cluster.

Description

Intelligent cluster suggestion system and method

The invention relates to an intelligent analysis system, in particular to an intelligent analysis system using a clustering technology. The invention also relates to an intelligent analysis method using clustering technology.

With the rapid development of Internet communications and computer technology, investors can use computers or smartphones to complete transactions in financial commodities (such as stocks) in real time. How to properly apply the transaction records of many investors in the trading system and cooperate with big data analysis technology to provide individual investors' investment behavior analysis and recommendations is the subject of this case.

Therefore, an object of the present invention is to provide an intelligent cluster analysis system.

Another object of the present invention is to provide an intelligent cluster analysis method.

Therefore, the intelligent cluster analysis system of the present invention includes a server unit and a terminal electronic device. The server unit stores customer transaction records related to a plurality of customers, respectively. The terminal electronic device can communicate with the server unit.

The server unit generates a plurality of transaction characteristic data related to the customers according to the transaction records of the customers. Each of the transaction characteristic data includes a plurality of transaction characteristic values. One of the transaction characteristic values is A total profit and loss.

The server unit generates a plurality of cluster definition data using a predetermined grouping algorithm based on the transaction characteristic data, and each of the cluster definition data includes a plurality of characteristic value ranges respectively related to the transaction characteristic values.

The server unit divides the transaction characteristic data into a plurality of clusters respectively corresponding to the cluster definition data according to the cluster definition data.

For each of the clusters, the server unit selects, among the transaction characteristic data of the cluster, the transaction characteristic information of which the percentile of the total profit or loss in the cluster meets a predetermined percentile range, respectively As multiple target transaction characteristic data.

The server unit generates, for each of the clusters, a reference material corresponding to the cluster according to the transaction characteristic values of the target transaction characteristic data of the cluster, and the reference material includes a plurality of references related to the cluster. The reference characteristic value of the characteristic value of the transaction.

The terminal electronic device sends an analysis request related to one of the clients to the server unit.

When the server unit receives the analysis request, the server unit transmits an analysis result to the terminal electronic device, and the analysis result includes at least part of the transaction characteristic values related to the transaction characteristic data of the target customer, and the related At least part of the reference characteristic values of the reference data corresponding to the cluster to which the transaction characteristic data of the target customer belongs.

When the terminal electronic device receives the analysis result, the terminal electronic device displays the analysis result.

In some implementations, the server unit determines a plurality of cluster attribute descriptions respectively related to the cluster definition data using a predetermined decision tree algorithm based on the feature value ranges. The analysis result also includes the cluster attribute description related to the cluster definition data corresponding to the transaction characteristic data of the target customer.

In some implementation aspects, the server unit further stores a plurality of financial commodity historical data respectively related to a plurality of financial commodities. The server unit generates a plurality of product characteristic data respectively related to the financial products based on the historical data of the financial products, and each of the product characteristic data includes a plurality of product characteristic values. The terminal electronic device sends a recommendation request related to the target customer to the server unit, the recommendation request including at least one target characteristic value related to one of the transaction characteristic values of the analysis result. When the server unit receives the suggestion request, the server unit generates at least one of the financial commodities related to the financial commodities according to the commodity characteristic data, the at least one target characteristic value, and the transaction characteristic data related to the target customer. The product list of the user, and transmits the product list to the terminal electronic device. When the terminal electronic device receives the product list, the terminal electronic device displays the product list.

In some implementation aspects, the predetermined group algorithm is a k-average algorithm.

In some embodiments, the terminal electronic device displays the analysis result in the form of a radar chart.

The intelligent cluster analysis method of the present invention is implemented by an intelligent cluster analysis system. The intelligent cluster analysis system includes a server unit and a terminal electronic device. The server unit stores customer transaction records related to multiple customers. The terminal electronic device can communicate with the server unit, and the method includes: the server unit generates a plurality of transaction characteristic data respectively related to the customers according to the transaction records of the customers, and each of the transaction characteristic data includes multiple One transaction characteristic value, one of the transaction characteristic values is a total profit and loss; the server unit generates a plurality of cluster definition data based on the transaction characteristic data using a predetermined group algorithm, Each of which includes a plurality of characteristic value ranges respectively related to the transaction characteristic values; the server unit divides the transaction characteristic data into a plurality of clusters respectively corresponding to the cluster definition data according to the cluster definition data; The server unit selects, for each of the clusters, among the transaction characteristic data of the cluster. The transaction characteristic data of which the percentile of the total profit or loss in the cluster meets a predetermined percentile range is respectively regarded as a plurality of target transaction characteristic data; the server unit targets each of the clusters according to the cluster The transaction characteristic values of the target transaction characteristic data of the generated a reference material corresponding to the cluster, and the reference material contains a plurality of reference characteristic values respectively related to the transaction characteristic values; the terminal electronic device transmits a correlation An analysis request from one of the customers of the target is sent to the server unit; when the server unit receives the analysis request, the server unit sends an analysis result to the terminal electronic device, and the analysis result includes information related to the target At least part of the transaction characteristic values of the transaction characteristic information of the customer, and at least part of the reference characteristic values of the reference information corresponding to the cluster to which the transaction characteristic information of the target customer belongs; and when the terminal electronics The device receives the analysis result, and the terminal electronic device displays the analysis result.

The effect of the present invention is that the analysis result includes at least part of the transaction characteristic values related to the transaction characteristic data of the target customer, and the transaction corresponding to the cluster to which the transaction characteristic data related to the target customer belongs. At least part of the reference characteristic values of the reference materials, so that the target customer can compare the difference between the transaction characteristic values and the reference characteristic values as a reference for adjusting its own investment behavior. In addition, the server unit responds The suggestion request generates the product list, so that the target customer can refer to the product list to adjust its investment behavior, so that the target customer's investment behavior is closer to the customer with better total profit and loss in the same cluster.

Referring to FIG. 1, an embodiment of the intelligent cluster analysis system 100 of the present invention includes a server unit 1 and a terminal electronic device 2.

The server unit 1 stores customer transaction records D1 (such as records of customers buying and selling stocks) that are related to multiple customers, and a plurality of financial commodity historical data D2 (such as ex-rights) that are respectively related to multiple financial products (such as stocks). Information, number of shares outstanding, etc.).

The terminal electronic device 2 can communicate with the server unit 1 via a communication network. The terminal electronic device 2 is, for example, a smart phone, a tablet computer, a desktop computer, or a laptop computer, but is not limited thereto.

The steps of the intelligent cluster analysis system 100 executing a data analysis program are described below with reference to FIGS. 1 and 2. First, as shown in step S01, the server unit 1 generates a plurality of transaction characteristic data related to the customers according to the customer transaction records D1, and generates a plurality of data related to the financial commodity historical data D2, respectively. Product characteristics of other financial commodities.

Each of the transaction characteristic data includes a plurality of transaction characteristic values. These transaction characteristic values are, for example, a total profit and loss, an investment win ratio, a risk tolerance, an investment frequency, an asset capacity, a speculative level, a total transaction days and a total transaction amount, but the items of these transaction characteristic values And the number is not limited to this.

Each of the commodity characteristic data includes a plurality of commodity characteristic values. The characteristic values of these commodities are, for example, a degree of risk, a liquidity rate, a volatility rate, a revenue, a chip, a yield rate, and a technology, but the items and number of the characteristic values of these commodities are not limited thereto.

Next, as shown in step S02, the server unit 1 generates a plurality of cluster definition data using a predetermined group algorithm based on the transaction characteristic data. Each of the cluster definition data includes a plurality of characteristic value ranges that are respectively related to the characteristic values of the transactions. In this embodiment, the predetermined clustering algorithm is k-means clustering.

Next, as shown in step S03, the server unit 1 divides the transaction characteristic data into a plurality of clusters respectively corresponding to the cluster definition data according to the cluster definition data.

Then, as shown in step S04, the server unit 1 determines a plurality of cluster attribute descriptions respectively related to the cluster definition data by using a predetermined decision tree algorithm according to the feature value ranges. These cluster attribute descriptions are, for example, "high risk type", "stock raising type", "stable operating type", etc., but are not limited thereto.

Next, as shown in step S05, the server unit 1 selects, for each of the clusters, among the transaction characteristic data of the cluster, the percentile of the total profit or loss in the cluster to meet a predetermined percentage. These range of transaction characteristic data are used as multiple target transaction characteristic data, respectively. In this embodiment, the predetermined percentile range is, for example, 50th to 100th, but is not limited thereto.

Finally, as shown in step S06, the server unit 1 generates, for each of the clusters, a reference material corresponding to the cluster according to the transaction characteristic values of the target transaction characteristic data of the cluster. The reference material contains a plurality of reference characteristic values that are respectively related to the transaction characteristic values.

The intelligent cluster analysis system 100 may execute the data analysis program every predetermined time to update the analysis results.

The steps of the intelligent cluster analysis system 100 executing a data feedback procedure are described below with reference to FIGS. 1 and 3. First, as shown in step S11, the terminal electronic device 2 transmits an analysis request related to one of the target customers to the server unit 1 according to the operation of the target customer.

Next, as shown in step S12, when the server unit 1 receives the analysis request, the server unit 1 transmits an analysis result to the terminal electronic device 2. The analysis result includes at least part of the transaction characteristic data related to the target customer's transaction characteristic data (in this embodiment, the investment win rate, the risk tolerance, the investment frequency, the asset capacity and the speculative degree are total). Five transaction characteristic values), at least part of the reference characteristic values (in this embodiment, the investment win rate, the risk tolerance) of the reference material corresponding to the cluster to which the transaction characteristic data of the target customer belongs , The investment frequency, the asset capacity, and the degree of speculation, a total of five reference characteristic values), and the cluster attribute description related to the cluster definition data related to the transaction characteristic data of the target customer.

Next, as shown in step S13, when the terminal electronic device 2 receives the analysis result, the terminal electronic device 2 displays the analysis result. As shown in FIG. 4, the terminal electronic device 2 displays the analysis result in the form of a radar chart, where the radar chart presents five transaction characteristic values (the The investment win rate, the risk tolerance, the investment frequency, the asset capacity and the degree of speculation are 20, 29, 43, 54 and 26 respectively, and the radar chart presents the transaction characteristics related to the target customer in this embodiment The five reference characteristic values (the investment win rate, the risk tolerance, the investment frequency, the asset capacity, and the degree of speculation) of the reference material corresponding to the cluster to which the data belongs are 70, 52, 43, 54 and 73, respectively . In this way, the target customer can compare the differences between the transaction characteristic values and the reference characteristic values as a reference for adjusting its investment behavior.

Then, as shown in step S14, the terminal electronic device 2 transmits a recommendation request related to the target customer to the server unit 1 according to the operation of the target customer, and the recommendation request includes at least one related to the analysis result. The target characteristic value of one of the transaction characteristic values. As shown in FIG. 5, the target customer can drag the value of the investment winning ratio from the position of 20 to the position of 70 by operating the terminal electronic device 2 to set the target characteristic value to 70.

Then, as shown in step S15, when the server unit 1 receives the suggestion request, the server unit 1 according to the product characteristic data, the at least one target characteristic value, and the transaction characteristic data related to the target customer, A commodity list is generated for at least one of the financial commodities.

Then, as shown in step S16, the server unit 1 transmits the product list to the terminal electronic device 2.

Finally, as shown in step S17, when the terminal electronic device 2 receives the product list, the terminal electronic device 2 displays the product list. If the target customer purchases the at least one financial commodity related to the product list with reference to the product list, at least one transaction characteristic value of the transaction characteristic data related to the target customer will approach the at least one target characteristic value (For example, the investment win ratio will change from 20 to close to 70). In this way, the target customer can adjust its investment behavior by referring to the product list, so that the target customer's investment behavior is closer to the customer with better total profit and loss in the same cluster.

In summary, in the embodiment of the intelligent cluster analysis system 100 of the present invention, the analysis result includes at least part of the transaction characteristic values related to the transaction characteristic data of the target customer, and the transaction related to the target customer. At least part of the reference characteristic values of the reference material corresponding to the cluster to which the characteristic data belongs, so that the target customer can compare the difference between the transaction characteristic values and the reference characteristic values as a reference for adjusting its own investment behavior In addition, the server unit 1 generates the product list in response to the suggestion request, so that the target customer can refer to the product list to adjust its own investment behavior so that the target customer's investment behavior is closer to the total in the same cluster. Customers with better profit and loss can indeed achieve the purpose of cost invention.

However, the above are only examples of the present invention. When the scope of implementation of the present invention cannot be limited by this, any simple equivalent changes and modifications made according to the scope of the patent application and the contents of the patent specification of the present invention are still Within the scope of the invention patent.

100‧‧‧ Intelligent Cluster Analysis System

1‧‧‧Server Unit

D1‧‧‧Customer transaction records

D2‧‧‧Financial Commodities Historical Data

2‧‧‧ terminal electronics

S01 ~ S06‧‧‧step

S01 ~ S17‧‧‧step

Other features and effects of the present invention will be clearly presented in the embodiment with reference to the drawings, wherein: FIG. 1 is a schematic diagram of a hardware connection relationship of an embodiment of the intelligent cluster analysis system of the present invention; FIG. 2 is the implementation A flowchart of the example illustrates the steps of a data analysis program; FIG. 3 is another flowchart of the embodiment, illustrating the steps of a data feedback program; and FIG. 4 and FIG. 5 are a radar chart of the embodiment, respectively.

Claims (8)

An intelligent cluster analysis system includes: a server unit storing customer transaction records related to multiple customers; and a terminal electronic device capable of communicating with the server unit; the server unit is based on the customer transaction records Generating a plurality of transaction characteristic data related to the customers, each of which includes a plurality of transaction characteristic values, and one of the transaction characteristic values is a total profit and loss; the server unit according to the And other transaction characteristic data, using a predetermined grouping algorithm to generate a plurality of cluster definition data, each of the cluster definition data includes a plurality of characteristic value ranges respectively related to the transaction characteristic values; the server unit according to the Etc. The cluster characteristic data is divided into a plurality of clusters corresponding to the cluster definition data; the server unit uses a predetermined decision tree algorithm to determine a plurality of A cluster attribute description of the cluster definition data; the server unit for each of the clusters in the cluster Among the transaction characteristic data, the transaction characteristic data of which the percentile of the total profit or loss in the cluster meets a predetermined percentile range is selected as a plurality of target transaction characteristic data; the server unit targets the clusters. Each of which generates a reference material corresponding to the cluster according to the transaction characteristic values of the target transaction characteristic data of the cluster, and the reference material includes a plurality of reference characteristic values respectively related to the transaction characteristic values ; The terminal electronic device sends an analysis request related to one of the clients to the server unit; when the server unit receives the analysis request, the server unit sends an analysis result to the terminal electronic device, The analysis result includes at least a portion of the transaction characteristic values of the transaction characteristic data related to the target customer, and at least a portion of the reference characteristics of the reference data corresponding to the cluster to which the transaction characteristic data related to the target customer belongs. Value, and the cluster to which the cluster definition data associated with the transaction characteristic data related to the target customer corresponds Attribute Description; terminal when the electronic device receives the analysis result, the terminal electronic device displays the analysis result. The intelligent cluster analysis system according to claim 1, wherein the server unit further stores a plurality of financial commodity historical data respectively related to a plurality of financial commodities; the server unit generates a plurality of financial commodity historical data according to the financial commodity historical data Product characteristic data related to the financial commodities, each of which includes a plurality of product characteristic values; the terminal electronic device sends a request for a proposal related to the target customer to the server unit, the proposal The request includes at least one target characteristic value of one of the transaction characteristic values related to the analysis result; when the server unit receives the suggestion request, the server unit according to the product characteristic data, the at least one target characteristic Value and transaction characteristic data related to the target customer, generate a product list related to at least one of the financial products, and transmit the product list to the terminal electronic device; when the terminal electronic device receives the product list , The terminal electronic device displays the product list. The intelligent cluster analysis system according to claim 1, wherein the predetermined cluster algorithm is a k-average algorithm. The intelligent cluster analysis system according to claim 1, wherein the terminal electronic device displays the analysis result in the form of a radar chart. An intelligent clustering analysis method is implemented by an intelligent clustering analysis system. The intelligent clustering analysis system includes a server unit and a terminal electronic device. The server unit stores customer transaction records related to multiple customers. The terminal The electronic device can communicate with the server unit. The method includes: the server unit generates a plurality of transaction characteristic data related to the customers according to the customer transaction records, and each of the transaction characteristic data includes a plurality of Transaction characteristic value, one of the transaction characteristic values is a total profit and loss; the server unit generates a plurality of cluster definition data based on the transaction characteristic data using a predetermined group algorithm, and each of the cluster definition data One includes a plurality of characteristic value ranges respectively related to the transaction characteristic values; the server unit divides the transaction characteristic data into a plurality of clusters respectively corresponding to the cluster definition data according to the cluster definition data; the The server unit uses a predetermined decision tree algorithm to determine multiple A description of the cluster properties of the cluster definition data; the server unit, for each of the clusters, selects, in the transaction characteristic data of the cluster, the percentile of the total profit or loss in the cluster meets a predetermined The transaction characteristic data in the percentile range are respectively used as a plurality of target transaction characteristic data; the server unit targets each of the clusters according to the transaction characteristic values of the target transaction characteristic data of the cluster, Generate a reference material corresponding to the cluster, the reference material contains a plurality of reference characteristic values respectively related to the transaction characteristic values; the terminal electronic device sends an analysis request related to one of the target of the customers to the server Server unit; when the server unit receives the analysis request, the server unit transmits an analysis result to the terminal electronic device, and the analysis result includes at least part of the transaction characteristic values related to the transaction characteristic data of the target customer At least part of the reference data corresponding to the cluster to which the target customer's transaction characteristic data belongs Consider the characteristic value and the cluster attribute description related to the cluster definition data that the transaction characteristic data related to the target customer meets; and when the terminal electronic device receives the analysis result, the terminal electronic device displays the analysis result . The intelligent cluster analysis method according to claim 5, wherein the server unit further stores a plurality of financial commodity historical data respectively related to a plurality of financial commodities, and the method further includes: the server unit according to the financial commodities The historical data generates a plurality of product characteristic data related to the financial commodities, each of which includes a plurality of product characteristic values; the terminal electronic device sends a suggestion request related to the target customer to the server Server unit, the suggestion request includes at least one target feature value of one of the transaction feature values related to the analysis result; when the server unit receives the suggestion request, the server unit according to the product feature data, The at least one target characteristic value and transaction characteristic data related to the target customer, generating a product list related to at least one of the financial commodities, and transmitting the product list to the terminal electronic device; and when the terminal electronic device The device receives the product list, and the terminal electronic device displays the product list. The intelligent cluster analysis method according to claim 5, wherein the predetermined cluster algorithm is a k-average algorithm. The intelligent cluster analysis method according to claim 5, wherein the terminal electronic device displays the analysis result in the form of a radar chart.